Overview

Dataset statistics

Number of variables32
Number of observations11000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.0 MiB
Average record size in memory288.0 B

Variable types

Categorical12
Numeric20

Alerts

device_fraud_count has constant value "0"Constant
foreign_request is highly imbalanced (76.4%)Imbalance
source is highly imbalanced (92.7%)Imbalance
device_distinct_emails_8w is highly imbalanced (79.5%)Imbalance
name_email_similarity has unique valuesUnique
intended_balcon_amount has unique valuesUnique
velocity_6h has unique valuesUnique
velocity_24h has unique valuesUnique
bank_branch_count_8w has 2072 (18.8%) zerosZeros
employment_status has 8517 (77.4%) zerosZeros
housing_status has 4117 (37.4%) zerosZeros
month has 1431 (13.0%) zerosZeros

Reproduction

Analysis started2023-07-17 08:03:00.525783
Analysis finished2023-07-17 08:03:51.211011
Duration50.69 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

fraud_bool
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size171.9 KiB
1
5563 
0
5437 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 5563
50.6%
0 5437
49.4%

Length

2023-07-17T14:33:51.258912image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-17T14:33:51.365429image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 5563
50.6%
0 5437
49.4%

Most occurring characters

ValueCountFrequency (%)
1 5563
50.6%
0 5437
49.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 5563
50.6%
0 5437
49.4%

Most occurring scripts

ValueCountFrequency (%)
Common 11000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 5563
50.6%
0 5437
49.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 5563
50.6%
0 5437
49.4%

income
Real number (ℝ)

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.62448182
Minimum0.1
Maximum0.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size171.9 KiB
2023-07-17T14:33:51.454815image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.1
Q10.4
median0.7
Q30.9
95-th percentile0.9
Maximum0.9
Range0.8
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.28574602
Coefficient of variation (CV)0.45757301
Kurtosis-0.95373007
Mean0.62448182
Median Absolute Deviation (MAD)0.2
Skewness-0.69435403
Sum6869.3
Variance0.081650791
MonotonicityNot monotonic
2023-07-17T14:33:51.558903image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0.9 3642
33.1%
0.8 1549
14.1%
0.1 1337
 
12.2%
0.6 1124
 
10.2%
0.7 1057
 
9.6%
0.4 734
 
6.7%
0.2 602
 
5.5%
0.5 519
 
4.7%
0.3 436
 
4.0%
ValueCountFrequency (%)
0.1 1337
 
12.2%
0.2 602
 
5.5%
0.3 436
 
4.0%
0.4 734
 
6.7%
0.5 519
 
4.7%
0.6 1124
 
10.2%
0.7 1057
 
9.6%
0.8 1549
14.1%
0.9 3642
33.1%
ValueCountFrequency (%)
0.9 3642
33.1%
0.8 1549
14.1%
0.7 1057
 
9.6%
0.6 1124
 
10.2%
0.5 519
 
4.7%
0.4 734
 
6.7%
0.3 436
 
4.0%
0.2 602
 
5.5%
0.1 1337
 
12.2%

name_email_similarity
Real number (ℝ)

Distinct11000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.44111586
Minimum0.00012759616
Maximum0.99994437
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size171.9 KiB
2023-07-17T14:33:51.693805image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.00012759616
5-th percentile0.05223386
Q10.16477102
median0.4114788
Q30.71544016
95-th percentile0.90152057
Maximum0.99994437
Range0.99981678
Interquartile range (IQR)0.55066913

Descriptive statistics

Standard deviation0.29455472
Coefficient of variation (CV)0.66774909
Kurtosis-1.2951735
Mean0.44111586
Median Absolute Deviation (MAD)0.26468663
Skewness0.25713883
Sum4852.2745
Variance0.086762481
MonotonicityNot monotonic
2023-07-17T14:33:51.819748image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5163112021 1
 
< 0.1%
0.2310501671 1
 
< 0.1%
0.4505612344 1
 
< 0.1%
0.7315751542 1
 
< 0.1%
0.1091316863 1
 
< 0.1%
0.3518231429 1
 
< 0.1%
0.9896280272 1
 
< 0.1%
0.8071411167 1
 
< 0.1%
0.1732926823 1
 
< 0.1%
0.7380368031 1
 
< 0.1%
Other values (10990) 10990
99.9%
ValueCountFrequency (%)
0.0001275961639 1
< 0.1%
0.0001320001488 1
< 0.1%
0.0003207064906 1
< 0.1%
0.0004202202632 1
< 0.1%
0.001091112057 1
< 0.1%
0.001164303707 1
< 0.1%
0.001694179769 1
< 0.1%
0.00178109231 1
< 0.1%
0.001864978382 1
< 0.1%
0.001998679021 1
< 0.1%
ValueCountFrequency (%)
0.999944372 1
< 0.1%
0.9999420374 1
< 0.1%
0.9999371691 1
< 0.1%
0.9999326052 1
< 0.1%
0.9998576294 1
< 0.1%
0.9998515095 1
< 0.1%
0.9997988941 1
< 0.1%
0.9997533842 1
< 0.1%
0.9997471305 1
< 0.1%
0.9996934134 1
< 0.1%

prev_address_months_count
Real number (ℝ)

Distinct255
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.238545
Minimum-1
Maximum357
Zeros0
Zeros (%)0.0%
Negative8931
Negative (%)81.2%
Memory size171.9 KiB
2023-07-17T14:33:51.950696image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median-1
Q3-1
95-th percentile82
Maximum357
Range358
Interquartile range (IQR)0

Descriptive statistics

Standard deviation38.105192
Coefficient of variation (CV)3.3905804
Kurtosis28.732584
Mean11.238545
Median Absolute Deviation (MAD)0
Skewness4.8754861
Sum123624
Variance1452.0056
MonotonicityNot monotonic
2023-07-17T14:33:52.081308image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1 8931
81.2%
11 80
 
0.7%
29 74
 
0.7%
28 72
 
0.7%
27 72
 
0.7%
10 65
 
0.6%
26 65
 
0.6%
30 63
 
0.6%
31 61
 
0.6%
12 59
 
0.5%
Other values (245) 1458
 
13.3%
ValueCountFrequency (%)
-1 8931
81.2%
7 2
 
< 0.1%
8 9
 
0.1%
9 36
 
0.3%
10 65
 
0.6%
11 80
 
0.7%
12 59
 
0.5%
13 33
 
0.3%
14 11
 
0.1%
15 2
 
< 0.1%
ValueCountFrequency (%)
357 1
 
< 0.1%
354 1
 
< 0.1%
344 2
< 0.1%
341 1
 
< 0.1%
337 2
< 0.1%
336 2
< 0.1%
335 1
 
< 0.1%
333 3
< 0.1%
331 1
 
< 0.1%
330 1
 
< 0.1%
Distinct394
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.12773
Minimum-1
Maximum408
Zeros71
Zeros (%)0.6%
Negative29
Negative (%)0.3%
Memory size171.9 KiB
2023-07-17T14:33:52.224091image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile5
Q135
median72
Q3144
95-th percentile291
Maximum408
Range409
Interquartile range (IQR)109

Descriptive statistics

Standard deviation88.283725
Coefficient of variation (CV)0.88171107
Kurtosis1.0611475
Mean100.12773
Median Absolute Deviation (MAD)49
Skewness1.2309949
Sum1101405
Variance7794.0161
MonotonicityNot monotonic
2023-07-17T14:33:52.372011image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 141
 
1.3%
6 137
 
1.2%
7 119
 
1.1%
8 110
 
1.0%
4 108
 
1.0%
9 105
 
1.0%
3 101
 
0.9%
41 100
 
0.9%
47 99
 
0.9%
10 98
 
0.9%
Other values (384) 9882
89.8%
ValueCountFrequency (%)
-1 29
 
0.3%
0 71
0.6%
1 84
0.8%
2 85
0.8%
3 101
0.9%
4 108
1.0%
5 141
1.3%
6 137
1.2%
7 119
1.1%
8 110
1.0%
ValueCountFrequency (%)
408 1
 
< 0.1%
392 1
 
< 0.1%
390 1
 
< 0.1%
389 1
 
< 0.1%
388 2
 
< 0.1%
387 1
 
< 0.1%
386 3
< 0.1%
385 2
 
< 0.1%
384 6
0.1%
383 1
 
< 0.1%

customer_age
Real number (ℝ)

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.327273
Minimum10
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size171.9 KiB
2023-07-17T14:33:52.492248image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile20
Q130
median40
Q350
95-th percentile60
Maximum90
Range80
Interquartile range (IQR)20

Descriptive statistics

Standard deviation13.041862
Coefficient of variation (CV)0.34939232
Kurtosis-0.28053792
Mean37.327273
Median Absolute Deviation (MAD)10
Skewness0.35149664
Sum410600
Variance170.09017
MonotonicityNot monotonic
2023-07-17T14:33:52.585552image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
30 2998
27.3%
40 2795
25.4%
50 2145
19.5%
20 1939
17.6%
60 761
 
6.9%
70 181
 
1.6%
10 143
 
1.3%
80 35
 
0.3%
90 3
 
< 0.1%
ValueCountFrequency (%)
10 143
 
1.3%
20 1939
17.6%
30 2998
27.3%
40 2795
25.4%
50 2145
19.5%
60 761
 
6.9%
70 181
 
1.6%
80 35
 
0.3%
90 3
 
< 0.1%
ValueCountFrequency (%)
90 3
 
< 0.1%
80 35
 
0.3%
70 181
 
1.6%
60 761
 
6.9%
50 2145
19.5%
40 2795
25.4%
30 2998
27.3%
20 1939
17.6%
10 143
 
1.3%

days_since_request
Real number (ℝ)

Distinct10999
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.070869
Minimum3.4723237 × 10-6
Maximum73.933618
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size171.9 KiB
2023-07-17T14:33:52.714136image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum3.4723237 × 10-6
5-th percentile0.0013124423
Q10.0064535575
median0.014147702
Q30.025029747
95-th percentile5.2621708
Maximum73.933618
Range73.933614
Interquartile range (IQR)0.018576189

Descriptive statistics

Standard deviation5.5679728
Coefficient of variation (CV)5.1994902
Kurtosis99.348294
Mean1.070869
Median Absolute Deviation (MAD)0.0087223887
Skewness9.0126521
Sum11779.559
Variance31.002321
MonotonicityNot monotonic
2023-07-17T14:33:53.082044image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.01513786537 2
 
< 0.1%
0.004868909347 1
 
< 0.1%
3.340344796 1
 
< 0.1%
0.02105261157 1
 
< 0.1%
3.837261701 1
 
< 0.1%
0.02469494805 1
 
< 0.1%
0.0005300713233 1
 
< 0.1%
0.002199762644 1
 
< 0.1%
0.01203083506 1
 
< 0.1%
0.02048840541 1
 
< 0.1%
Other values (10989) 10989
99.9%
ValueCountFrequency (%)
3.472323747 × 10-61
< 0.1%
8.042419741 × 10-61
< 0.1%
1.001736607 × 10-51
< 0.1%
1.209476512 × 10-51
< 0.1%
1.73992793 × 10-51
< 0.1%
1.87444327 × 10-51
< 0.1%
2.46826403 × 10-51
< 0.1%
2.516164907 × 10-51
< 0.1%
3.122192288 × 10-51
< 0.1%
3.254205672 × 10-51
< 0.1%
ValueCountFrequency (%)
73.93361787 1
< 0.1%
73.52381727 1
< 0.1%
73.25135381 1
< 0.1%
73.15856398 1
< 0.1%
73.13761833 1
< 0.1%
72.82551663 1
< 0.1%
72.72199579 1
< 0.1%
72.71181508 1
< 0.1%
72.54571101 1
< 0.1%
72.54241036 1
< 0.1%

intended_balcon_amount
Real number (ℝ)

Distinct11000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.3204108
Minimum-9.1004434
Maximum111.53811
Zeros0
Zeros (%)0.0%
Negative8945
Negative (%)81.3%
Memory size171.9 KiB
2023-07-17T14:33:53.211416image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-9.1004434
5-th percentile-1.5835761
Q1-1.1876052
median-0.87516426
Q3-0.45265277
95-th percentile49.480155
Maximum111.53811
Range120.63855
Interquartile range (IQR)0.73495238

Descriptive statistics

Standard deviation18.702226
Coefficient of variation (CV)2.9590206
Kurtosis10.836404
Mean6.3204108
Median Absolute Deviation (MAD)0.35410103
Skewness3.1405795
Sum69524.519
Variance349.77325
MonotonicityNot monotonic
2023-07-17T14:33:53.347247image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.9207989547 1
 
< 0.1%
-1.425798367 1
 
< 0.1%
-0.9537137753 1
 
< 0.1%
-1.141589265 1
 
< 0.1%
-0.6284556426 1
 
< 0.1%
-1.328951209 1
 
< 0.1%
-1.052179959 1
 
< 0.1%
-0.9203403301 1
 
< 0.1%
-0.8553284665 1
 
< 0.1%
19.70736419 1
 
< 0.1%
Other values (10990) 10990
99.9%
ValueCountFrequency (%)
-9.100443415 1
< 0.1%
-8.249791727 1
< 0.1%
-6.739347652 1
< 0.1%
-6.100095987 1
< 0.1%
-5.830054744 1
< 0.1%
-5.603924177 1
< 0.1%
-5.41265749 1
< 0.1%
-4.643764114 1
< 0.1%
-4.09851765 1
< 0.1%
-4.024131634 1
< 0.1%
ValueCountFrequency (%)
111.5381075 1
< 0.1%
111.3212718 1
< 0.1%
110.660004 1
< 0.1%
109.83029 1
< 0.1%
109.8097179 1
< 0.1%
109.626686 1
< 0.1%
109.1652665 1
< 0.1%
108.9902385 1
< 0.1%
108.8366566 1
< 0.1%
108.71952 1
< 0.1%

payment_type
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size171.9 KiB
1
4073 
2
3535 
0
2087 
3
1304 
4
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row2
2nd row1
3rd row0
4th row2
5th row2

Common Values

ValueCountFrequency (%)
1 4073
37.0%
2 3535
32.1%
0 2087
19.0%
3 1304
 
11.9%
4 1
 
< 0.1%

Length

2023-07-17T14:33:53.465240image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-17T14:33:53.579083image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 4073
37.0%
2 3535
32.1%
0 2087
19.0%
3 1304
 
11.9%
4 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
1 4073
37.0%
2 3535
32.1%
0 2087
19.0%
3 1304
 
11.9%
4 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 4073
37.0%
2 3535
32.1%
0 2087
19.0%
3 1304
 
11.9%
4 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 11000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 4073
37.0%
2 3535
32.1%
0 2087
19.0%
3 1304
 
11.9%
4 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 4073
37.0%
2 3535
32.1%
0 2087
19.0%
3 1304
 
11.9%
4 1
 
< 0.1%

zip_count_4w
Real number (ℝ)

Distinct3398
Distinct (%)30.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1591.8215
Minimum26
Maximum6368
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size171.9 KiB
2023-07-17T14:33:53.698094image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum26
5-th percentile491
Q1905
median1295
Q32003
95-th percentile3635
Maximum6368
Range6342
Interquartile range (IQR)1098

Descriptive statistics

Standard deviation993.59188
Coefficient of variation (CV)0.6241855
Kurtosis1.9467996
Mean1591.8215
Median Absolute Deviation (MAD)481
Skewness1.3848414
Sum17510036
Variance987224.82
MonotonicityNot monotonic
2023-07-17T14:33:53.823762image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
905 17
 
0.2%
963 17
 
0.2%
942 16
 
0.1%
1073 16
 
0.1%
1130 15
 
0.1%
846 15
 
0.1%
1126 15
 
0.1%
973 14
 
0.1%
1101 14
 
0.1%
785 14
 
0.1%
Other values (3388) 10847
98.6%
ValueCountFrequency (%)
26 1
< 0.1%
36 1
< 0.1%
37 1
< 0.1%
44 1
< 0.1%
45 1
< 0.1%
54 1
< 0.1%
56 1
< 0.1%
65 1
< 0.1%
73 1
< 0.1%
76 1
< 0.1%
ValueCountFrequency (%)
6368 1
< 0.1%
6310 1
< 0.1%
6219 1
< 0.1%
6084 1
< 0.1%
6079 1
< 0.1%
6012 1
< 0.1%
5977 1
< 0.1%
5953 1
< 0.1%
5942 1
< 0.1%
5934 1
< 0.1%

velocity_6h
Real number (ℝ)

Distinct11000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5397.0709
Minimum3.9741117
Maximum16084.617
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size171.9 KiB
2023-07-17T14:33:53.958078image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum3.9741117
5-th percentile1140.2143
Q13153.297
median5076.4437
Q37376.2453
95-th percentile10569.834
Maximum16084.617
Range16080.643
Interquartile range (IQR)4222.9483

Descriptive statistics

Standard deviation2954.5748
Coefficient of variation (CV)0.54744043
Kurtosis0.046002652
Mean5397.0709
Median Absolute Deviation (MAD)2091.3884
Skewness0.58138147
Sum59367780
Variance8729512.3
MonotonicityNot monotonic
2023-07-17T14:33:54.090464image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3070.757247 1
 
< 0.1%
7804.068809 1
 
< 0.1%
5499.612457 1
 
< 0.1%
6925.712099 1
 
< 0.1%
7928.395064 1
 
< 0.1%
6514.62238 1
 
< 0.1%
5092.945989 1
 
< 0.1%
1804.710851 1
 
< 0.1%
1524.50679 1
 
< 0.1%
6292.093525 1
 
< 0.1%
Other values (10990) 10990
99.9%
ValueCountFrequency (%)
3.974111674 1
< 0.1%
64.42257069 1
< 0.1%
109.0064806 1
< 0.1%
109.5186213 1
< 0.1%
127.6809123 1
< 0.1%
189.9345641 1
< 0.1%
190.3590837 1
< 0.1%
194.5696249 1
< 0.1%
196.6109025 1
< 0.1%
198.6393816 1
< 0.1%
ValueCountFrequency (%)
16084.61717 1
< 0.1%
15885.00546 1
< 0.1%
15797.22302 1
< 0.1%
15738.88634 1
< 0.1%
15684.45759 1
< 0.1%
15476.16173 1
< 0.1%
15471.95176 1
< 0.1%
15460.04484 1
< 0.1%
15408.49659 1
< 0.1%
15362.03832 1
< 0.1%

velocity_24h
Real number (ℝ)

Distinct11000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4694.3423
Minimum1390.9297
Maximum9335.9392
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size171.9 KiB
2023-07-17T14:33:54.230539image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1390.9297
5-th percentile2536.248
Q13500.7419
median4712.6998
Q35674.974
95-th percentile7225.5858
Maximum9335.9392
Range7945.0095
Interquartile range (IQR)2174.2321

Descriptive statistics

Standard deviation1451.4516
Coefficient of variation (CV)0.30919169
Kurtosis-0.39520619
Mean4694.3423
Median Absolute Deviation (MAD)1082.3487
Skewness0.30392394
Sum51637765
Variance2106711.9
MonotonicityNot monotonic
2023-07-17T14:33:54.372571image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3409.637279 1
 
< 0.1%
5210.640026 1
 
< 0.1%
3276.111218 1
 
< 0.1%
4996.124284 1
 
< 0.1%
4838.547139 1
 
< 0.1%
5990.927107 1
 
< 0.1%
6168.310115 1
 
< 0.1%
2843.480033 1
 
< 0.1%
5905.708419 1
 
< 0.1%
2929.907576 1
 
< 0.1%
Other values (10990) 10990
99.9%
ValueCountFrequency (%)
1390.929683 1
< 0.1%
1470.933784 1
< 0.1%
1480.948716 1
< 0.1%
1508.767989 1
< 0.1%
1565.912514 1
< 0.1%
1566.382455 1
< 0.1%
1629.089317 1
< 0.1%
1644.879957 1
< 0.1%
1685.945142 1
< 0.1%
1686.193816 1
< 0.1%
ValueCountFrequency (%)
9335.939178 1
< 0.1%
9313.494634 1
< 0.1%
9241.893566 1
< 0.1%
9238.92164 1
< 0.1%
9209.79189 1
< 0.1%
9167.75513 1
< 0.1%
9142.120114 1
< 0.1%
9132.148491 1
< 0.1%
9126.232169 1
< 0.1%
9108.131954 1
< 0.1%

velocity_4w
Real number (ℝ)

Distinct10999
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4802.4541
Minimum2863.7833
Maximum6886.1304
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size171.9 KiB
2023-07-17T14:33:54.523698image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2863.7833
5-th percentile3106.1277
Q14234.881
median4870.2115
Q35447.8701
95-th percentile6565.1067
Maximum6886.1304
Range4022.347
Interquartile range (IQR)1212.9891

Descriptive statistics

Standard deviation941.29811
Coefficient of variation (CV)0.19600356
Kurtosis-0.43344995
Mean4802.4541
Median Absolute Deviation (MAD)615.82967
Skewness-0.0043449331
Sum52826995
Variance886042.14
MonotonicityNot monotonic
2023-07-17T14:33:54.670311image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4255.233507 2
 
< 0.1%
4483.238269 1
 
< 0.1%
4217.949113 1
 
< 0.1%
3752.043359 1
 
< 0.1%
4920.321753 1
 
< 0.1%
4904.687695 1
 
< 0.1%
4377.905508 1
 
< 0.1%
5045.141367 1
 
< 0.1%
3149.403633 1
 
< 0.1%
6596.107938 1
 
< 0.1%
Other values (10989) 10989
99.9%
ValueCountFrequency (%)
2863.783336 1
< 0.1%
2994.070713 1
< 0.1%
2998.639977 1
< 0.1%
3002.534134 1
< 0.1%
3004.933661 1
< 0.1%
3005.026296 1
< 0.1%
3005.356813 1
< 0.1%
3006.831393 1
< 0.1%
3007.208178 1
< 0.1%
3008.14587 1
< 0.1%
ValueCountFrequency (%)
6886.130363 1
< 0.1%
6884.723493 1
< 0.1%
6883.610981 1
< 0.1%
6878.703205 1
< 0.1%
6876.157271 1
< 0.1%
6875.679407 1
< 0.1%
6871.552509 1
< 0.1%
6867.25914 1
< 0.1%
6866.074501 1
< 0.1%
6864.578021 1
< 0.1%

bank_branch_count_8w
Real number (ℝ)

Distinct1060
Distinct (%)9.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean157.05736
Minimum0
Maximum2251
Zeros2072
Zeros (%)18.8%
Negative0
Negative (%)0.0%
Memory size171.9 KiB
2023-07-17T14:33:54.825670image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median7
Q317.25
95-th percentile1436.05
Maximum2251
Range2251
Interquartile range (IQR)16.25

Descriptive statistics

Standard deviation440.10039
Coefficient of variation (CV)2.8021634
Kurtosis8.5686384
Mean157.05736
Median Absolute Deviation (MAD)7
Skewness3.1034809
Sum1727631
Variance193688.35
MonotonicityNot monotonic
2023-07-17T14:33:54.952168image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2072
18.8%
1 2044
18.6%
2 654
 
5.9%
10 337
 
3.1%
11 333
 
3.0%
12 325
 
3.0%
14 294
 
2.7%
9 292
 
2.7%
13 291
 
2.6%
8 270
 
2.5%
Other values (1050) 4088
37.2%
ValueCountFrequency (%)
0 2072
18.8%
1 2044
18.6%
2 654
 
5.9%
3 150
 
1.4%
4 123
 
1.1%
5 136
 
1.2%
6 167
 
1.5%
7 187
 
1.7%
8 270
 
2.5%
9 292
 
2.7%
ValueCountFrequency (%)
2251 1
< 0.1%
2246 1
< 0.1%
2225 1
< 0.1%
2222 1
< 0.1%
2205 1
< 0.1%
2194 1
< 0.1%
2185 1
< 0.1%
2169 1
< 0.1%
2163 1
< 0.1%
2159 1
< 0.1%
Distinct34
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.5316364
Minimum0
Maximum34
Zeros41
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size171.9 KiB
2023-07-17T14:33:55.069825image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q15
median8
Q312
95-th percentile18
Maximum34
Range34
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.087479
Coefficient of variation (CV)0.59630753
Kurtosis0.52487382
Mean8.5316364
Median Absolute Deviation (MAD)3
Skewness0.784657
Sum93848
Variance25.882443
MonotonicityNot monotonic
2023-07-17T14:33:55.184470image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
8 971
 
8.8%
5 916
 
8.3%
7 894
 
8.1%
6 856
 
7.8%
2 776
 
7.1%
9 774
 
7.0%
4 704
 
6.4%
3 641
 
5.8%
10 606
 
5.5%
11 606
 
5.5%
Other values (24) 3256
29.6%
ValueCountFrequency (%)
0 41
 
0.4%
1 387
 
3.5%
2 776
7.1%
3 641
5.8%
4 704
6.4%
5 916
8.3%
6 856
7.8%
7 894
8.1%
8 971
8.8%
9 774
7.0%
ValueCountFrequency (%)
34 1
 
< 0.1%
32 3
 
< 0.1%
31 1
 
< 0.1%
30 6
 
0.1%
29 4
 
< 0.1%
28 6
 
0.1%
27 7
 
0.1%
26 8
 
0.1%
25 22
0.2%
24 27
0.2%

employment_status
Real number (ℝ)

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.47181818
Minimum0
Maximum6
Zeros8517
Zeros (%)77.4%
Negative0
Negative (%)0.0%
Memory size171.9 KiB
2023-07-17T14:33:55.293966image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.0894724
Coefficient of variation (CV)2.3090937
Kurtosis7.540787
Mean0.47181818
Median Absolute Deviation (MAD)0
Skewness2.7726719
Sum5190
Variance1.1869501
MonotonicityNot monotonic
2023-07-17T14:33:55.392684image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 8517
77.4%
1 1171
 
10.6%
2 661
 
6.0%
5 293
 
2.7%
3 212
 
1.9%
4 140
 
1.3%
6 6
 
0.1%
ValueCountFrequency (%)
0 8517
77.4%
1 1171
 
10.6%
2 661
 
6.0%
3 212
 
1.9%
4 140
 
1.3%
5 293
 
2.7%
6 6
 
0.1%
ValueCountFrequency (%)
6 6
 
0.1%
5 293
 
2.7%
4 140
 
1.3%
3 212
 
1.9%
2 661
 
6.0%
1 1171
 
10.6%
0 8517
77.4%

credit_risk_score
Real number (ℝ)

Distinct427
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean153.78782
Minimum-130
Maximum378
Zeros5
Zeros (%)< 0.1%
Negative109
Negative (%)1.0%
Memory size171.9 KiB
2023-07-17T14:33:55.513453image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-130
5-th percentile36
Q194
median145
Q3211
95-th percentile294
Maximum378
Range508
Interquartile range (IQR)117

Descriptive statistics

Standard deviation79.604707
Coefficient of variation (CV)0.51762687
Kurtosis-0.45303426
Mean153.78782
Median Absolute Deviation (MAD)58
Skewness0.21908064
Sum1691666
Variance6336.9094
MonotonicityNot monotonic
2023-07-17T14:33:55.648864image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 74
 
0.7%
97 71
 
0.6%
109 68
 
0.6%
112 64
 
0.6%
141 64
 
0.6%
81 64
 
0.6%
118 62
 
0.6%
138 62
 
0.6%
121 62
 
0.6%
103 62
 
0.6%
Other values (417) 10347
94.1%
ValueCountFrequency (%)
-130 1
< 0.1%
-118 1
< 0.1%
-117 1
< 0.1%
-115 1
< 0.1%
-110 1
< 0.1%
-106 1
< 0.1%
-103 1
< 0.1%
-101 1
< 0.1%
-97 1
< 0.1%
-95 1
< 0.1%
ValueCountFrequency (%)
378 2
< 0.1%
368 1
 
< 0.1%
364 3
< 0.1%
362 1
 
< 0.1%
360 1
 
< 0.1%
359 3
< 0.1%
358 2
< 0.1%
357 1
 
< 0.1%
356 2
< 0.1%
355 1
 
< 0.1%

email_is_free
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size171.9 KiB
1
6434 
0
4566 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 6434
58.5%
0 4566
41.5%

Length

2023-07-17T14:33:55.767076image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-17T14:33:55.869940image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 6434
58.5%
0 4566
41.5%

Most occurring characters

ValueCountFrequency (%)
1 6434
58.5%
0 4566
41.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 6434
58.5%
0 4566
41.5%

Most occurring scripts

ValueCountFrequency (%)
Common 11000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 6434
58.5%
0 4566
41.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 6434
58.5%
0 4566
41.5%

housing_status
Real number (ℝ)

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2935455
Minimum0
Maximum6
Zeros4117
Zeros (%)37.4%
Negative0
Negative (%)0.0%
Memory size171.9 KiB
2023-07-17T14:33:55.949463image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile4
Maximum6
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2893552
Coefficient of variation (CV)0.99676062
Kurtosis-0.33927996
Mean1.2935455
Median Absolute Deviation (MAD)1
Skewness0.74734357
Sum14229
Variance1.6624367
MonotonicityNot monotonic
2023-07-17T14:33:56.034959image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 4117
37.4%
2 3235
29.4%
1 2197
20.0%
4 1183
 
10.8%
3 256
 
2.3%
5 10
 
0.1%
6 2
 
< 0.1%
ValueCountFrequency (%)
0 4117
37.4%
1 2197
20.0%
2 3235
29.4%
3 256
 
2.3%
4 1183
 
10.8%
5 10
 
0.1%
6 2
 
< 0.1%
ValueCountFrequency (%)
6 2
 
< 0.1%
5 10
 
0.1%
4 1183
 
10.8%
3 256
 
2.3%
2 3235
29.4%
1 2197
20.0%
0 4117
37.4%

phone_home_valid
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size171.9 KiB
0
7369 
1
3631 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 7369
67.0%
1 3631
33.0%

Length

2023-07-17T14:33:56.138084image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-17T14:33:56.241661image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 7369
67.0%
1 3631
33.0%

Most occurring characters

ValueCountFrequency (%)
0 7369
67.0%
1 3631
33.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7369
67.0%
1 3631
33.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7369
67.0%
1 3631
33.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7369
67.0%
1 3631
33.0%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size171.9 KiB
1
9532 
0
1468 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 9532
86.7%
0 1468
 
13.3%

Length

2023-07-17T14:33:56.334893image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-17T14:33:56.443352image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 9532
86.7%
0 1468
 
13.3%

Most occurring characters

ValueCountFrequency (%)
1 9532
86.7%
0 1468
 
13.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 9532
86.7%
0 1468
 
13.3%

Most occurring scripts

ValueCountFrequency (%)
Common 11000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 9532
86.7%
0 1468
 
13.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 9532
86.7%
0 1468
 
13.3%

bank_months_count
Real number (ℝ)

Distinct30
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.544909
Minimum-1
Maximum31
Zeros0
Zeros (%)0.0%
Negative3511
Negative (%)31.9%
Memory size171.9 KiB
2023-07-17T14:33:56.537016image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median2
Q325
95-th percentile30
Maximum31
Range32
Interquartile range (IQR)26

Descriptive statistics

Standard deviation12.497221
Coefficient of variation (CV)1.1851426
Kurtosis-1.4473619
Mean10.544909
Median Absolute Deviation (MAD)3
Skewness0.52969414
Sum115994
Variance156.18055
MonotonicityNot monotonic
2023-07-17T14:33:56.649731image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
-1 3511
31.9%
1 1700
15.5%
28 875
 
8.0%
30 813
 
7.4%
15 560
 
5.1%
31 493
 
4.5%
25 454
 
4.1%
10 360
 
3.3%
2 330
 
3.0%
5 326
 
3.0%
Other values (20) 1578
14.3%
ValueCountFrequency (%)
-1 3511
31.9%
1 1700
15.5%
2 330
 
3.0%
3 56
 
0.5%
4 59
 
0.5%
5 326
 
3.0%
6 142
 
1.3%
7 9
 
0.1%
8 1
 
< 0.1%
9 58
 
0.5%
ValueCountFrequency (%)
31 493
4.5%
30 813
7.4%
29 85
 
0.8%
28 875
8.0%
27 64
 
0.6%
26 210
 
1.9%
25 454
4.1%
24 22
 
0.2%
23 1
 
< 0.1%
22 55
 
0.5%

has_other_cards
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size171.9 KiB
0
9290 
1
1710 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 9290
84.5%
1 1710
 
15.5%

Length

2023-07-17T14:33:57.037589image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-17T14:33:57.136114image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 9290
84.5%
1 1710
 
15.5%

Most occurring characters

ValueCountFrequency (%)
0 9290
84.5%
1 1710
 
15.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9290
84.5%
1 1710
 
15.5%

Most occurring scripts

ValueCountFrequency (%)
Common 11000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9290
84.5%
1 1710
 
15.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9290
84.5%
1 1710
 
15.5%

proposed_credit_limit
Real number (ℝ)

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean673.5
Minimum190
Maximum2100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size171.9 KiB
2023-07-17T14:33:57.214207image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum190
5-th percentile200
Q1200
median200
Q31500
95-th percentile1500
Maximum2100
Range1910
Interquartile range (IQR)1300

Descriptive statistics

Standard deviation592.96039
Coefficient of variation (CV)0.88041632
Kurtosis-0.90897981
Mean673.5
Median Absolute Deviation (MAD)0
Skewness0.81487568
Sum7408500
Variance351602.02
MonotonicityNot monotonic
2023-07-17T14:33:57.319667image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
200 5600
50.9%
1500 2406
21.9%
500 1487
 
13.5%
1000 863
 
7.8%
2000 426
 
3.9%
990 104
 
0.9%
510 53
 
0.5%
1900 40
 
0.4%
490 11
 
0.1%
2100 4
 
< 0.1%
Other values (2) 6
 
0.1%
ValueCountFrequency (%)
190 2
 
< 0.1%
200 5600
50.9%
210 4
 
< 0.1%
490 11
 
0.1%
500 1487
 
13.5%
510 53
 
0.5%
990 104
 
0.9%
1000 863
 
7.8%
1500 2406
21.9%
1900 40
 
0.4%
ValueCountFrequency (%)
2100 4
 
< 0.1%
2000 426
 
3.9%
1900 40
 
0.4%
1500 2406
21.9%
1000 863
 
7.8%
990 104
 
0.9%
510 53
 
0.5%
500 1487
13.5%
490 11
 
0.1%
210 4
 
< 0.1%

foreign_request
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size171.9 KiB
0
10576 
1
 
424

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 10576
96.1%
1 424
 
3.9%

Length

2023-07-17T14:33:57.436635image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-17T14:33:57.537952image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 10576
96.1%
1 424
 
3.9%

Most occurring characters

ValueCountFrequency (%)
0 10576
96.1%
1 424
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 10576
96.1%
1 424
 
3.9%

Most occurring scripts

ValueCountFrequency (%)
Common 11000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 10576
96.1%
1 424
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 10576
96.1%
1 424
 
3.9%

source
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size171.9 KiB
0
10902 
1
 
98

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 10902
99.1%
1 98
 
0.9%

Length

2023-07-17T14:33:57.622535image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-17T14:33:57.725485image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 10902
99.1%
1 98
 
0.9%

Most occurring characters

ValueCountFrequency (%)
0 10902
99.1%
1 98
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 10902
99.1%
1 98
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Common 11000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 10902
99.1%
1 98
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 10902
99.1%
1 98
 
0.9%

session_length_in_minutes
Real number (ℝ)

Distinct10980
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.8440526
Minimum-1
Maximum73.121739
Zeros0
Zeros (%)0.0%
Negative20
Negative (%)0.2%
Memory size171.9 KiB
2023-07-17T14:33:57.828004image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile1.25862
Q13.16565
median5.124298
Q38.7048974
95-th percentile24.900451
Maximum73.121739
Range74.121739
Interquartile range (IQR)5.5392473

Descriptive statistics

Standard deviation8.8295425
Coefficient of variation (CV)1.1256353
Kurtosis12.717207
Mean7.8440526
Median Absolute Deviation (MAD)2.4804212
Skewness3.1991865
Sum86284.579
Variance77.960821
MonotonicityNot monotonic
2023-07-17T14:33:57.957618image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1 20
 
0.2%
3.505441777 2
 
< 0.1%
2.562239636 1
 
< 0.1%
17.40650274 1
 
< 0.1%
71.53268013 1
 
< 0.1%
1.410006047 1
 
< 0.1%
9.113939573 1
 
< 0.1%
3.202724293 1
 
< 0.1%
0.9927125324 1
 
< 0.1%
3.150313666 1
 
< 0.1%
Other values (10970) 10970
99.7%
ValueCountFrequency (%)
-1 20
0.2%
0.06129668537 1
 
< 0.1%
0.07866006358 1
 
< 0.1%
0.09977437646 1
 
< 0.1%
0.1260002326 1
 
< 0.1%
0.1663926041 1
 
< 0.1%
0.1749702557 1
 
< 0.1%
0.2631363344 1
 
< 0.1%
0.2716220813 1
 
< 0.1%
0.2720401067 1
 
< 0.1%
ValueCountFrequency (%)
73.12173915 1
< 0.1%
71.53268013 1
< 0.1%
71.41097936 1
< 0.1%
71.0911553 1
< 0.1%
69.91195171 1
< 0.1%
69.63190873 1
< 0.1%
67.62100744 1
< 0.1%
67.17487352 1
< 0.1%
66.90271255 1
< 0.1%
66.01594349 1
< 0.1%

device_os
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size171.9 KiB
3
4644 
2
2886 
0
2772 
1
620 
4
 
78

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row2
3rd row0
4th row2
5th row0

Common Values

ValueCountFrequency (%)
3 4644
42.2%
2 2886
26.2%
0 2772
25.2%
1 620
 
5.6%
4 78
 
0.7%

Length

2023-07-17T14:33:58.086643image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-17T14:33:58.200633image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
3 4644
42.2%
2 2886
26.2%
0 2772
25.2%
1 620
 
5.6%
4 78
 
0.7%

Most occurring characters

ValueCountFrequency (%)
3 4644
42.2%
2 2886
26.2%
0 2772
25.2%
1 620
 
5.6%
4 78
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 4644
42.2%
2 2886
26.2%
0 2772
25.2%
1 620
 
5.6%
4 78
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Common 11000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 4644
42.2%
2 2886
26.2%
0 2772
25.2%
1 620
 
5.6%
4 78
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 4644
42.2%
2 2886
26.2%
0 2772
25.2%
1 620
 
5.6%
4 78
 
0.7%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size171.9 KiB
0
6036 
1
4964 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 6036
54.9%
1 4964
45.1%

Length

2023-07-17T14:33:58.307389image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-17T14:33:58.418681image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 6036
54.9%
1 4964
45.1%

Most occurring characters

ValueCountFrequency (%)
0 6036
54.9%
1 4964
45.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 6036
54.9%
1 4964
45.1%

Most occurring scripts

ValueCountFrequency (%)
Common 11000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 6036
54.9%
1 4964
45.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 6036
54.9%
1 4964
45.1%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size171.9 KiB
1
10226 
2
 
669
0
 
101
-1
 
4

Length

Max length2
Median length1
Mean length1.0003636
Min length1

Characters and Unicode

Total characters11004
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 10226
93.0%
2 669
 
6.1%
0 101
 
0.9%
-1 4
 
< 0.1%

Length

2023-07-17T14:33:58.508721image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-17T14:33:58.623199image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 10230
93.0%
2 669
 
6.1%
0 101
 
0.9%

Most occurring characters

ValueCountFrequency (%)
1 10230
93.0%
2 669
 
6.1%
0 101
 
0.9%
- 4
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11000
> 99.9%
Dash Punctuation 4
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 10230
93.0%
2 669
 
6.1%
0 101
 
0.9%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11004
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 10230
93.0%
2 669
 
6.1%
0 101
 
0.9%
- 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11004
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 10230
93.0%
2 669
 
6.1%
0 101
 
0.9%
- 4
 
< 0.1%
Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size171.9 KiB
0
11000 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11000
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 11000
100.0%

Length

2023-07-17T14:33:58.714612image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-17T14:33:58.811777image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 11000
100.0%

Most occurring characters

ValueCountFrequency (%)
0 11000
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 11000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 11000
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 11000
100.0%

month
Real number (ℝ)

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4207273
Minimum0
Maximum7
Zeros1431
Zeros (%)13.0%
Negative0
Negative (%)0.0%
Memory size171.9 KiB
2023-07-17T14:33:58.896256image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q35
95-th percentile7
Maximum7
Range7
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.2508168
Coefficient of variation (CV)0.65799365
Kurtosis-1.173544
Mean3.4207273
Median Absolute Deviation (MAD)2
Skewness0.037690776
Sum37628
Variance5.0661764
MonotonicityNot monotonic
2023-07-17T14:33:58.995765image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
3 1508
13.7%
4 1497
13.6%
2 1439
13.1%
0 1431
13.0%
5 1307
11.9%
1 1290
11.7%
6 1283
11.7%
7 1245
11.3%
ValueCountFrequency (%)
0 1431
13.0%
1 1290
11.7%
2 1439
13.1%
3 1508
13.7%
4 1497
13.6%
5 1307
11.9%
6 1283
11.7%
7 1245
11.3%
ValueCountFrequency (%)
7 1245
11.3%
6 1283
11.7%
5 1307
11.9%
4 1497
13.6%
3 1508
13.7%
2 1439
13.1%
1 1290
11.7%
0 1431
13.0%

Interactions

2023-07-17T14:33:48.144878image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:01.985289image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:04.568067image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:07.032248image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:09.595937image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:11.933992image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:14.446881image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:17.080102image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:19.322351image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:21.918986image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:24.285848image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:26.637602image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:29.201636image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:31.375612image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:33.748610image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:36.286705image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:38.524525image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:40.847711image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:43.307621image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:45.636380image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:48.254411image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:02.105137image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:04.685310image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:07.160643image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:09.718295image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:12.103634image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:14.565227image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:17.217400image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:19.459723image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:22.039830image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:24.438637image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2023-07-17T14:33:30.418649image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:32.661922image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:35.291871image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:37.532289image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:39.805916image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:42.345406image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:44.628119image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:46.900260image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:49.543904image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:03.593701image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:06.054101image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:08.683759image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:10.994754image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:13.480476image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:16.156842image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:18.441453image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:20.802954image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:23.326298image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:25.726901image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:28.259338image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:30.513194image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:32.763610image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:35.399680image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:37.630984image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:39.926023image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:42.446181image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:44.731424image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:47.001417image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:49.655428image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:03.715484image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:06.168127image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:08.797666image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:11.111183image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:13.595364image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:16.276111image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:18.551922image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:20.917696image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:23.457458image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:25.840822image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:28.383403image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:30.621061image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:32.877092image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:35.518100image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:37.744919image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:40.064515image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:42.559515image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:44.847222image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:47.110459image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:49.763208image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:03.840007image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:06.277779image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:08.909757image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:11.223261image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:13.713137image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:16.408934image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:18.655506image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:21.025213image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:23.574575image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:25.952566image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:28.496299image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:30.724458image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:32.989870image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:35.617412image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:37.850299image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:40.187279image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:42.667433image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:44.960383image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:47.222351image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:49.869905image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:03.965834image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:06.395040image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:09.016584image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:11.331848image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:13.864680image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:16.524537image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:18.756358image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:21.132114image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:23.681547image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:26.059319image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:28.605623image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:30.824005image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:33.096996image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:35.720725image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:37.953814image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:40.289726image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:42.770224image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:45.066543image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:47.333587image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:49.976325image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:04.085843image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:06.516309image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:09.130182image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:11.449263image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:13.977360image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:16.634794image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:18.861817image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:21.240229image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:23.795132image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:26.169136image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:28.714655image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:30.928266image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:33.208729image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:35.838653image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:38.063589image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:40.399854image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:42.875834image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:45.175176image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:47.445494image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:50.086824image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:04.201813image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:06.649961image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:09.242995image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:11.574994image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:14.090292image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:16.740934image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:18.969921image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:21.356013image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:23.917462image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:26.278406image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:28.827373image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:31.033925image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:33.343546image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:35.959332image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:38.186832image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:40.513578image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:42.981194image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:45.283170image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:47.556319image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:50.198556image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:04.328792image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:06.783385image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:09.364546image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:11.699068image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:14.204744image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:16.853217image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:19.082743image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:21.469136image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:24.036209image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:26.399352image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:28.960932image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:31.152456image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:33.474998image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:36.068015image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:38.297614image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:40.622031image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:43.084196image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:45.407296image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:47.669240image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:50.321193image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:04.451681image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:06.921313image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:09.483534image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:11.816718image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:14.331007image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:16.967458image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:19.195501image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:21.814862image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:24.157675image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:26.517162image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:29.083662image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:31.260803image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:33.614375image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:36.179277image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:38.417300image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:40.736908image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:43.196810image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:45.524009image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:33:48.030854image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Missing values

2023-07-17T14:33:50.542738image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-17T14:33:51.007086image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

fraud_boolincomename_email_similarityprev_address_months_countcurrent_address_months_countcustomer_agedays_since_requestintended_balcon_amountpayment_typezip_count_4wvelocity_6hvelocity_24hvelocity_4wbank_branch_count_8wdate_of_birth_distinct_emails_4wemployment_statuscredit_risk_scoreemail_is_freehousing_statusphone_home_validphone_mobile_validbank_months_counthas_other_cardsproposed_credit_limitforeign_requestsourcesession_length_in_minutesdevice_oskeep_alive_sessiondevice_distinct_emails_8wdevice_fraud_countmonth
761700.40.516311-137100.009813-0.92079929583070.7572473409.6372794483.238269090621401-10200.0002.56224000105
829110.30.791303-179200.001521-1.149205144607012.1689806880.6539766263.37862613165741201140200.0003.12468321100
780600.60.22560138162017.914128-1.26064608405349.9158694942.4630534112.826191175085120110200.00014.31663401106
184900.50.226115-189400.003949-1.100606212571497.5104063284.3356243416.993764161920101-10200.0004.86386220106
670300.50.881606327207.167611-1.124762221689200.5415082655.8339425552.1445771130-1181101-101500.0004.14990201102
18300.90.817490-1124300.024965-0.79577019894785.6612813648.7565824344.07246814602011101101000.0007.93863221106
967310.50.720748-1193300.013223-1.31880432023338.9806164708.9240364962.29823819023500012002000.0005.15490630104
79610.10.099846-158300.027699-0.82529515871803.8877352593.5478353859.9521041270119111140500.00012.14744201106
276600.70.86025313013200.0255827.077536136131065.3468534112.5759105215.474340671001320401301200.0003.37226421102
93210.90.597385-1111400.011480-0.92500818145723.0514804114.8476674286.24126824203001101601500.00011.16653930106
fraud_boolincomename_email_similarityprev_address_months_countcurrent_address_months_countcustomer_agedays_since_requestintended_balcon_amountpayment_typezip_count_4wvelocity_6hvelocity_24hvelocity_4wbank_branch_count_8wdate_of_birth_distinct_emails_4wemployment_statuscredit_risk_scoreemail_is_freehousing_statusphone_home_validphone_mobile_validbank_months_counthas_other_cardsproposed_credit_limitforeign_requestsourcesession_length_in_minutesdevice_oskeep_alive_sessiondevice_distinct_emails_8wdevice_fraud_countmonth
91210.90.234616-1197200.029852-0.684569313864119.5196406227.3016134706.90802528911851011270510.0002.20208610104
772310.90.155806-169300.009369-1.038297215601027.0449373619.7457164966.51091101202110001-101500.0004.15777431103
911900.60.214439-179500.026266-1.02802117802751.5594803012.7205533646.90954247601531011280200.0005.70610621106
922000.80.102112103500.02322199.73545623574636.0955912560.7085095098.1850091901960201211500.00017.48774120107
117210.90.160513-1265900.008021-0.83228317237276.5811075878.5893474762.721134283212521010101500.0001.02191730103
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